Training deep learning-based synthetic aperture radar automatic target recognition (SAR-ATR) systems for use in an “open-world” operating environment has, thus far proven difficult. Most SAR-ATR systems are designed to achieve maximum accuracy for a limited set of classes, yet ignore the implications of encountering novel target classes during deployment. Even worse, the standard deep learning training objectives fundamentally inherit a closed-world assumption, and provide no guidance for how to handle out-of-distribution (OOD) data. In this work, we develop a novel training procedure called adversarial outlier exposure (AdvOE) to codesign the ATR system for accuracy and OOD detection. Our method introduces a large, diverse, and unlabeled auxiliary training dataset containing samples from the OOD set. The AdvOE objective encourages a deep neural network to learn robust features of the in-distribution training data, while also promoting maximum entropy predictions for adversarially perturbed versions of the OOD data. We experiment with the recent SAMPLE dataset, and find our method nearly doubles the OOD detection performance over the baseline in key settings, and excels when using only synthetic training data. As compared to several other advanced ATR training techniques, AdvOE also affords significant improvements in both classification and detection statistics. Finally, we conduct extensive experiments that measure the effect of OOD set granularity on detection rates; discuss the implications of using different detection algorithms; and develop a novel analysis technique to validate our findings and interpret the OOD detection problem from a new perspective.

中文翻译：

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训练SAR-ATR模型以在开放世界环境中可靠运行

迄今为止，已经证明很难训练基于深度学习的合成孔径雷达自动目标识别（SAR-ATR）系统，以在“开放世界”操作环境中使用。大多数SAR-ATR系统旨在为一组有限的类实现最大的准确性，但忽略了在部署过程中遇到新颖目标类所带来的影响。更糟糕的是，标准的深度学习培训目标从根本上继承了封闭世界的假设，并且没有提供有关如何处理分布外（OOD）数据的指导。在这项工作中，我们开发了一种称为对抗性离群值暴露（AdvOE）的新颖训练程序，以对ATR系统进行代码签名以进行准确性和OOD检测。我们的方法引入了一个庞大，多样且未标记的辅助训练数据集，其中包含来自OOD集合的样本。AdvOE目标鼓励深度神经网络学习分布内训练数据的鲁棒特征，同时还促进OOD数据的对抗性摄动版本的最大熵预测。我们对最近的SAMPLE数据集进行了试验，发现我们的方法在关键设置中的基线OOD检测性能几乎翻了一番，并且在仅使用综合训练数据时表现出色。与其他几种高级ATR培训技术相比，AdvOE在分类和检测统计方面也提供了显着改进。最后，我们进行了广泛的实验，以测量OOD设置粒度对检测率的影响；讨论使用不同检测算法的含义；